对于依赖单个情况下的观测AB相设计数据的变换距离测试:蒙特卡洛模拟研究
Anouk Vroegindeweij1, Linde N Nijhof2, Patrick Onghena3
1Department of Pediatric Rheumatology/Immunology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Behavior research methods
|August 1, 2023
概括
变距离测试 (PDT) 有效地评估单个病例观测数据中的治疗效果,显示出高功率和可靠的错误率,特别是在更多的观测和更高的自身相关性的情况下.
科学领域:
- 统计 统计 统计 统计
- 行为科学 行为科学
- 单个案例研究设计
背景情况:
- 在具有依赖性的单个病例观测设计 (SCOD) 数据中评估治疗效应具有挑战性.
- 现有的方法,如单个案例随机化测试 (SCRT) 和传统的排列测试,在功率和错误控制方面存在局限性,特别是自相关性.
研究的目的:
- 引入和评估用于分析依赖 SCOD AB 阶段数据的变换距离测试 (PDT).
- 将PDT的统计功率和I型错误率与SCRT和传统的排列试验进行比较.
主要方法:
- 使用蒙特卡洛模拟来估计PDT功率和I型错误率.
- 通过各种治疗效果水平,自身相关性水平和观察数 (30,60,90,120) 来模拟数据.
- 与单个案例随机化试验 (SCRT) 和传统的排列试验进行了比较.
主要成果:
- PDT证明了足够的功率 (≥80%) 来检测中等治疗效应,在30个观察结果中,自相关性 ≤ .45.
- 在60次观测时,PDT无论自相关性如何,都达到足够的功率;在≥90次观测时,它检测到小效应,自相关性≤0.30.
- PDT保持了可接受的I型错误率 (≤5%的≥60个观测和自相对应率<.60),在功率和错误控制方面超过了SCRT和传统的排列测试.
结论:
- 变距离测试 (PDT) 是一种强大的非参数方法,用于分析没有线性趋势的依赖SCOD AB相数据.
- 与现有方法相比,PDT提供了更好的功率和I型错误控制,特别是在存在自相关性和较少观察的情况下.
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